*Dataset with singles*

*Load data*
clear

use "Data\sample1_new.dta"

*Keep only relevant variables
keep pnr aar wage_growth_ambition final_educ wage_start_mean_ambition grad_region educ_eika fined civst faelle_nr koen aegte_nr individual erhvervsindk_13


forvalues i=81(1)85{
	
*9th grade
gen temp_g=wage_growth_ambition if final_educ==1109`i'
egen temp_g_2=max(temp_g)
replace wage_growth_ambition=temp_g_2 if (final_educ==1007 | final_educ==1008 | final_educ==1023 | final_educ==1123 | final_educ==1009 | final_educ==1022) & grad_region==`i' 
drop temp_g temp_g_2

gen temp_w=wage_start_mean_ambition if final_educ==1109`i'
egen temp_w_2=max(temp_w)
replace wage_start_mean_ambition=temp_w_2 if (final_educ==1007 | final_educ==1008 | final_educ==1023 | final_educ==1123 | final_educ==1009 | final_educ==1022) & grad_region==`i' 
drop temp_w temp_w_2

replace final_educ=1107`i' if final_educ==1107 & grad_region==`i'
replace final_educ=1008`i' if final_educ==1008 & grad_region==`i'
replace final_educ=1023`i' if final_educ==1023 & grad_region==`i'
replace final_educ=1123`i' if final_educ==1123 & grad_region==`i'
replace final_educ=1009`i' if final_educ==1009 & grad_region==`i'
replace final_educ=1022`i' if final_educ==1022 & grad_region==`i'

*10th grade
gen temp_g=wage_growth_ambition if final_educ==1110`i'
egen temp_g_2=max(temp_g)
replace wage_growth_ambition=temp_g_2 if final_educ==1010 & grad_region==`i' 
drop temp_g temp_g_2

gen temp_w=wage_start_mean_ambition if final_educ==1110`i'
egen temp_w_2=max(temp_w)
replace wage_start_mean_ambition=temp_w_2 if final_educ==1010 & grad_region==`i' 
drop temp_w temp_w_2

replace final_educ=1010`i' if final_educ==1010 & grad_region==`i'

}

*3.g 
gen temp_g=wage_growth_ambition if final_educ==1198
egen temp_g_2=max(temp_g)
replace wage_growth_ambition=temp_g_2 if final_educ==1097
drop temp_g temp_g_2

gen temp_w=wage_start_mean_ambition if final_educ==1198
egen temp_w_2=max(temp_w)
replace wage_start_mean_ambition=temp_w_2 if final_educ==1097
drop temp_w temp_w_2

**kmeans**

*standardize variables
sum wage_start_mean_ambition
sca the_mean_s=r(mean)
sca the_sd_s=r(sd)
gen wage_start_mean_ambition_s=(wage_start_mean_ambition-the_mean_s)/the_sd_s
sum wage_start_mean_ambition_s

sum wage_growth_ambition
sca the_mean_g=r(mean)
sca the_sd_g=r(sd)
gen wage_growth_ambition_s=(wage_growth_ambition-the_mean_g)/the_sd_g
sum wage_growth_ambition_s

cluster kmeans wage_growth_ambition_s, k(4) name(ambition_type_only_g) s(kr(1234))
tab ambition_type_only_g
tabstat wage_growth_ambition_s, by(ambition_type_only_g)

tab ambition_type_only_g fined, row

*Define marital status
gen relationship=0
replace relationship=1 if civst=="G" | (civst!="G" & faelle_nr!="")

gen married=0
replace married=1 if civst=="G"

gen cohab=0
replace cohab=1 if relationship==1 & married==0

*Make couple ID based on the PNR of the man (maybe check later if we get the same by using PNR of the woman)
gen couple_id="."
replace couple_id=pnr if koen=="1" & relationship==1
replace couple_id=aegte_nr if koen=="2" & married==1
replace couple_id=faelle_nr if koen=="2" & cohab==1
sort couple_id aar

*Keep only couples where we observe both partners
gen temp2=koen if relationship==1
destring temp2, replace
by couple_id aar: egen temp3=mean(temp2)
keep if (temp3>1 & temp3<2 & relationship==1) | relationship==0
drop temp2 temp3

sort pnr aar

**Keep only those for who we observe ambition type**
keep if wage_growth_ambition!=.

*Keep only couples where we observe both partners
gen temp2=koen if relationship==1
destring temp2, replace
by couple_id aar, sort: egen temp3=mean(temp2)
keep if (temp3==1.5 & relationship==1) | relationship==0
drop temp2 temp3

sort pnr aar


*Save*
save "Results\tab_B3\Scenario_7\dataset_with_singles_only_g.dta", replace
